UC Riverside

Metabolic engineering improves industrial biochemical production through beneficial edits in the microbial host’s genome. Traditional metabolic engineering approaches modify the relevant biosynthesis pathway or induce genomic mutations in response to environmental stress, often within a few well-characterized host strains. With the advent of next generation sequencing and new synthetic biology tools (e.g., CRISPR genome editing), more microbes could be sequenced and engineered with a genome-wide approach as novel bioproduction hosts. Further sequencing technology improvements and cost decreases have recently made it economically feasible to sequence collections of many microbial genomes. These large genomic datasets could be used to determine the genetic underpinnings of industrially-relevant phenotypes and identify non-intuitive genome-wide metabolic engineering targets.This study focuses on developing Pseudomonas chlororaphis as a biotechnology host for the production of phenazines, its colorful, redox-active secondary metabolites which have current and potential uses as agricultural fungicides and within bioectrochemical devices. First, I experimentally characterized the phenazine production, biofilm formation, and temperature tolerance of 34 strains and used that phenotype data to identify the best phenazine production strain. Next, I sequenced all strains with both Illumina and Oxford Nanopore technologies and assembled their genomes with multiple state-of-the-art assembly algorithms (i.e., SPAdes, Unicycler, Flye) to ensure I had optimal genomic input for downstream analysis. The final genome assemblies and phenazine phenotype data were passed to the microbial GWAS algorithm DBGWAS, which provided a list of 330 hits statistically associated with phenazine production. Finally, I overexpressed the 7 coding sequences (CDSs) associated with the top phenotype scores within strain DSM 21509, the strain which naturally has the highest phenazine-1-carboxamide titers. I confirmed that 3 of these CDSs, a putative transcriptional repressor, carboxypeptidase, and histidine transporter, significantly affected phenazine-1-carboxamide titers. This work demonstrates the potential of a population genomics-guided approach to metabolic engineering, which identifies engineering targets from a collection of genomes rather than a single genomic reference.